Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Empatica). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Moger, Michael. Classification of Arbitrary Motion into a Canonical Basis.

Degree: MS, 2018, University of New Hampshire

The Empatica E4 wristwatch utilizes four sensors to capture medical data from its user - an accelerometer, a plethysmograph, an electro-dermal activity sensor, and an infrared thermophile. Utilizing these sensors, the device can provide detection-based feedback for patients suffering from various ailments. However, each sensor is coupled with the other readings, so any raw data will have a degree of noise accompanying the actual signal. After detailing a conceptual and programming knowledge of various industry-standard data processing techniques, we follow the appropriate steps to take in order to clean up a noisy E4 data signal, starting with supervised basis signals and ending with unsupervised, random samples. We conclude with a discussion of how one can decompose arbitrary motions into a canonical basis for proper data analysis, providing insight based on our results. Advisors/Committee Members: Kevin M Short, Mark E Lyon, Rita A Hibschweiler.

Subjects/Keywords: Empatica; Machine learning; Matching pursuit; Signal classification; Singular value decomposition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Moger, M. (2018). Classification of Arbitrary Motion into a Canonical Basis. (Thesis). University of New Hampshire. Retrieved from https://scholars.unh.edu/thesis/1258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Moger, Michael. “Classification of Arbitrary Motion into a Canonical Basis.” 2018. Thesis, University of New Hampshire. Accessed July 04, 2020. https://scholars.unh.edu/thesis/1258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Moger, Michael. “Classification of Arbitrary Motion into a Canonical Basis.” 2018. Web. 04 Jul 2020.

Vancouver:

Moger M. Classification of Arbitrary Motion into a Canonical Basis. [Internet] [Thesis]. University of New Hampshire; 2018. [cited 2020 Jul 04]. Available from: https://scholars.unh.edu/thesis/1258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Moger M. Classification of Arbitrary Motion into a Canonical Basis. [Thesis]. University of New Hampshire; 2018. Available from: https://scholars.unh.edu/thesis/1258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Grundtman, Per. Adaptive Learning.

Degree: Electrical and Space Engineering, 2017, Luleå University of Technology

The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.

Subjects/Keywords: adaptive learning; machine learning; e-learning; biosyncing; biometric sensors; Empatica E4; Intelligent Tutoring Systems; WEKA; Computer and Information Sciences; Data- och informationsvetenskap; Engineering and Technology; Teknik och teknologier

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Grundtman, P. (2017). Adaptive Learning. (Thesis). Luleå University of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Grundtman, Per. “Adaptive Learning.” 2017. Thesis, Luleå University of Technology. Accessed July 04, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Grundtman, Per. “Adaptive Learning.” 2017. Web. 04 Jul 2020.

Vancouver:

Grundtman P. Adaptive Learning. [Internet] [Thesis]. Luleå University of Technology; 2017. [cited 2020 Jul 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Grundtman P. Adaptive Learning. [Thesis]. Luleå University of Technology; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.